Development of new NIR-spectroscopy method combined with multivariate analysis for detection of adulteration in camel milk with goat milk

A Systematic Review of Spectroscopic Techniques for Detecting Milk Adulteration

Milk adulteration is a crucial worldwide concern that endangers food safety and public health, as it involves the deliberate tampering with milk by adding foreign substances or removing essential nutrients, often to boost profits or hinder microbial growth. Traditional detection methods frequently lack the sensitivity and speed required to identify adulterants within milk's complex matrix. This systematic review critically examines the application of spectroscopic techniques for detecting milk adulteration, focusing on Nuclear Magnetic Resonance (NMR), Infrared (IR) Spectroscopy, Raman Spectroscopy, Ultraviolet-Visible (UV-Vis) Spectroscopy, Mass Spectrometry, Laser-Based Techniques, Dielectric Spectroscopy, and X-Ray Spectroscopy. Each technique's principles, advantages, limitations, and specific applications in identifying adulterants, such as water, urea, melamine, added sugars, fats, preservatives, and heavy metals are discussed. The review highlights how these methods offer rapid, non-destructive, and sensitive analysis, enhancing the ability to detect adulterants at molecular levels. Despite advancements, challenges persist, including the complexity and natural variability of milk composition, high costs of advanced equipment, need for specialized expertise, and lack of standardized protocols. Future directions emphasize developing portable and cost-effective spectroscopic devices, integrating artificial intelligence and machine learning for advanced data analysis, and fostering international collaboration to establish standardized methodologies and comprehensive spectral databases. By addressing these challenges, spectroscopic techniques can be more widely implemented, ultimately safeguarding public health, ensuring the integrity of dairy products, and maintaining consumer trust in the global food supply chain.

Ultrafast and Ultrasensitive Bacterial Detection in Biofluids: Leveraging Resazurin as a Visible and Fluorescent Spectroscopic Marker

Here we report the application of chemometric analysis for modeling absorbance spectroscopy and fluorescence emission data from a resazurin-based assay targeting low-level bacterial detection in biofluids. Bacteria spiked samples were incubated with resazurin and absorbance and fluorescence data were collected at 30 min intervals. The absorbance data was subjected to Principal Component Analysis (PCA) and Partial Least Squares Regression (PLSR) and compared with the univariate fluorescence spectroscopy approach. The analysis demonstrated the multidimensional nature of the absorbance data, highlighting the appearance of the resorufin peak at the 2 h time point with a low bacterial inoculum of 0.01 CFU mL-1 across all the samples tested-water, urine and serum. The PLSR models supported the PCA data and exhibited strong predictive capabilities for water (RC2 = 0.937, RCV2 = 0.934), urine (RC2 = 0.899, RCV2 = 0.880) and serum (RC2 = 0.985, RCV2 = 0.967). Conversely, fluorescence is contingent upon resorufin existence, necessitating a prolonged waiting period postincubation with resazurin to verify the presence of bacteria, especially when contamination levels are low. Given the substantial global impact of bacteria-related infections, this method detects bacteria at low concentrations precisely and rapidly, improving efficiency and adaptability for point-of-care settings, promising swift diagnosis of bacterial infections, environmental monitoring, or food-quality control.

Rapid Classification of Milk Using a Cost-Effective Near Infrared Spectroscopy Device and Variable Cluster-Support Vector Machine (VC-SVM) Hybrid Models

Removing fat from whole milk and adding water to milk to increase its volume are among the most common food fraud practices that alter the characteristics of milk. Usually, deviations from the expected fat content can indicate adulteration. Infrared spectroscopy is a commonly used technique for distinguishing pure milk from adulterated milk, even when it comes from different animal species. More recently, portable spectrometers have enabled in situ analysis with analytical performance comparable to that of benchtop instruments. Partial Least Square (PLS) analysis is the most popular tool for developing calibration models, although the increasing availability of portable near infrared spectroscopy (NIRS) has led to the use of alternative supervised techniques, including support vector machine (SVM). The aim of this study was to develop and implement a method based on the combination of a compact and low-cost Fourier Transform near infrared (FT-NIR) spectrometer and variable cluster-support vector machine (VC-SVM) hybrid model for the rapid classification of milk in accordance with EU Regulation EC No. 1308/2013 without any pre-treatment. The results obtained from the external validation of the VC-SVM hybrid model showed a perfect classification capacity (100% sensitivity, 100% specificity, MCC = 1) for the radial basis function (RBF) kernel when used to classify whole vs. not-whole and skimmed vs. not-skimmed milk samples. A strong classification capacity (94.4% sensitivity, 100% specificity, MCC = 0.95) was also achieved in discriminating semi-skimmed vs. not-semi-skimmed milk samples. This approach provides the dairy industry with a practical, simple and efficient solution to quickly identify skimmed, semi-skimmed and whole milk and detect potential fraud.

Double-Tube Multiplex TaqMan Real-Time PCR for the Detection of Eight Animal-Derived Dairy Ingredients

Milk and dairy products represent important sources of nutrition in our daily lives. The identification of species within dairy products holds importance for monitoring food adulteration and ensuring traceability. This study presented a method that integrated double-tube and duplex real-time polymerase chain reaction (PCR) with multiplex TaqMan probes to enable the high-throughput detection of animal-derived ingredients in milk and dairy products. The detection system utilized one pair of universal primers, two pairs of specific primers, and eight animal-derived specific probes for cow, buffalo, goat, sheep, camel, yak, horse, and donkey. These components were optimized within a double-tube and four-probe PCR multiplex system. The developed double-tube detection system could simultaneously identify the above eight targets with a detection limit of 10-0.1 pg/μL. Validation using simulated adulterated milk samples demonstrated a detection limit of 0.1%. The primary advantage of this method lies in the simplification of the multiplex quantitative real-time PCR (qPCR) system through the use of universal primers. This method provides an efficient approach for detecting ingredients in dairy products, providing powerful technical support for market supervision.

Advances of machine learning-assisted small extracellular vesicles detection strategy

Detection of extracellular vesicles (EVs), particularly small EVs (sEVs), is of great significance in exploring their physiological characteristics and clinical applications. The heterogeneity of sEVs plays a crucial role in distinguishing different types of cells and diseases. Machine learning, with its exceptional data processing capabilities, offers a solution to overcome the limitations of conventional detection methods for accurately classifying sEV subtypes and sources. Principal component analysis, linear discriminant analysis, partial least squares discriminant analysis, XGBoost, support vector machine, k-nearest neighbor, and deep learning, along with some combined methods such as principal component-linear discriminant analysis, have been successfully applied in the detection and identification of sEVs. This review focuses on machine learning-assisted detection strategies for cell identification and disease prediction via sEVs, and summarizes the integration of these strategies with surface-enhanced Raman scattering, electrochemistry, inductively coupled plasma mass spectrometry and fluorescence. The performance of different machine learning-based detection strategies is compared, and the advantages and limitations of various machine learning models are also evaluated. Finally, we discuss the merits and limitations of the current approaches and briefly outline the perspective of potential research directions in the field of sEV analysis based on machine learning.

Identification of Milk Adulteration in Camel Milk Using FT-Mid-Infrared Spectroscopy and Machine Learning Models

Camel milk, esteemed for its high nutritional value, has long been a subject of interest. However, the adulteration of camel milk with cow milk poses a significant threat to food quality and safety. Fourier-transform infrared spectroscopy (FT-MIR) has emerged as a rapid method for the detection and quantification of cow milk adulteration. Nevertheless, its effectiveness in conveniently detecting adulteration in camel milk remains to be determined. Camel milk samples were collected from Alxa League, Inner Mongolia, China, and were supplemented with varying concentrations of cow milk samples. Spectra were acquired using the FOSS FT6000 spectrometer, and a diverse set of machine learning models was employed to detect cow milk adulteration in camel milk. Our results demonstrate that the Linear Discriminant Analysis (LDA) model effectively distinguishes pure camel milk from adulterated samples, maintaining a 100% detection rate even at cow milk addition levels of 10 g/100 g. The neural network quantitative model for cow milk adulteration in camel milk exhibited a detection limit of 3.27 g/100 g and a quantification limit of 10.90 g/100 g. The quantitative model demonstrated excellent precision and accuracy within the range of 10-90 g/100 g of adulteration. This study highlights the potential of FT-MIR spectroscopy in conjunction with machine learning techniques for ensuring the authenticity and quality of camel milk, thus addressing concerns related to food integrity and consumer safety.

Possible Alternatives: Identifying and Quantifying Adulteration in Buffalo, Goat, and Camel Milk Using Mid-Infrared Spectroscopy Combined with Modern Statistical Machine Learning Methods

Adulteration of higher priced milks with cheaper ones to obtain extra profit can adversely affect consumer health and the market. In this study, pure buffalo milk (BM), goat milk (GM), camel milk (CM), and their mixtures with 5-50% (vol/vol) cow milk or water were used. Mid-infrared spectroscopy (MIRS) combined with modern statistical machine learning was used for the discrimination and quantification of cow milk or water adulteration in BM, GM, and CM. Compared to partial least squares (PLS), modern statistical machine learning-especially support vector machines (SVM), projection pursuit regression (PPR), and Bayesian regularized neural networks (BRNN)-exhibited superior performance for the detection of adulteration. The best prediction models for the different predictive traits are as follows: The binary classification models developed by SVM resulted in differentiation of CM-cow milk, and GM/CM-water mixtures. PLS resulted in differentiation of BM/GM-cow milk and BM-water mixtures. All of the above models have 100% classification accuracy. SVM was used to develop multi-classification models for identifying the high and low proportions of cow milk in BM, GM, and CM, as well as the high and low proportions of water adulteration in BM and GM, with correct classification rates of 94%, 100%, 100%, 99%, and 100%, respectively. In addition, a PLS-based model was developed for identifying the high and low proportions of water adulteration in CM, with correct classification rates of 100%. A regression model for quantifying cow milk in BM was developed using PCA + BRNN, with RMSEV = 5.42%, and RV2 = 0.88. A regression model for quantifying water adulteration in BM was developed using PCA + PPR, with RMSEV = 1.70%, and RV2 = 0.99. Modern statistical machine learning improved the accuracy of MIRS in predicting BM, GM, and CM adulteration more effectively than PLS.

Evaluation of the adulteration of camel milk by non-camel milk using multispectral image, fluorescence and infrared spectroscopy

In the present study, the focus was to evaluate the potential of three spectroscopic techniques (Middle Infrared -MIR-, fluorescence, and multispectral imaging -MSI-) to check the level of adulteration in camel milk with goat, cow, and ewe milks. Camel milk was adulterated with goat, ewe, and cow milks, respectively, at 6 different levels viz. 0.5, 1, 2, 5, 10, and 15%. After preprocessing the data with standard normal variate (SNV), multiplicative scattering correction (MSC), and normalization (area under spectrum = 1), partial least squares regression (PLSR) and partial least squares discriminant analysis (PLSDA) were used to predict the adulteration level and their belonging group, respectively. The PLSR and PLSDA models, validated using external data, highlighted that fluorescence spectroscopy was the most accurate technique giving a Rp² ranging between 0.63 and 0.96 and an accuracy ranging between 67 and 83%. However, no technique has allowed the construction of robust PLSR and PLSDA models for the simultaneous prediction of contamination of camel milk by the three milks.

Feasibility study of detecting some milk adulterations using a LED-based Vis-SWNIR photoacoustic spectroscopy system

The aim of this study is to evaluate a previousely developed photoacoustic spectroscopy system with light sources of visible to short-wave near infrared (Vis-SWNIR, 395-940 nm) for detection of adulterations in cow's milk including formalin, urea, hydrogen peroxide, starch, sodium hypochlorite, and detergent powder. The results of principal component analysis (PCA) showed a very good visual differentiation of different adulterations. The artificial neural networks (ANN) showed the highest classification accuracy (97.6 %) in detection of adulteration type and adulteration level (nearly 100 %). It can be generally concluded that the Vis-SWNIR photoacoustic spectroscopy system is a reliable and potent instrument for detecting various types of milk adulterations. Further studies are suggested with including cow's milk of different sources with probable variations in composition to generalize the findings of the present study. With the extension of the light sources to the range of long-wave NIR, the system can be applied as a diagnostic tool for quality evaluation of other liquid foods.

N-glycan profiles as a tool in qualitative and quantitative analysis of goat milk adulteration

Goat milk is closer to human milk in some respects than cow milk, and therefore preferred by many consumers. Because of the short lactation period and consequently less milk production of goats, the price of goat milk is often higher than that of cow milk, so that adulteration of goat milk is common. N-glycans have stability and thus have a good potential for acting as a new biomarker for identifying dairy adulteration. In this study, the N-glycan structures of goat milk and cow milk were analyzed by Ultra-high performance liquid chromatography (UPLC) and MALDI-TOF-MS. Based on the high species specificity of N-glycans, a method for identifying goat milk mixed with cow milk was established. The adulteration content of 5% cow milk in goat milk could be qualitatively and quantitatively detected. A prediction model of adulteration in goat milk was established by using partial least squares (PLS).

Detection of protein, starch, oil, and moisture content of corn kernels using one-dimensional convolutional autoencoder and near-infrared spectroscopy

Background

Analysis of the nutritional values and chemical composition of grain products plays an essential role in determining the quality of the products. Near-infrared spectroscopy has attracted the attention of researchers in recent years due to its advantages in the analysis process. However, preprocessing and regression models in near-infrared spectroscopy are usually determined by trial and error. Combining newly popular deep learning algorithms with near-infrared spectroscopy has brought a new perspective to this area.

Methods

This article presents a new method that combines a one-dimensional convolutional autoencoder with near-infrared spectroscopy to analyze the protein, moisture, oil, and starch content of corn kernels. First, a one-dimensional convolutional autoencoder model was created for three different spectra in the corn dataset. Thirty-two latent variables were obtained for each spectrum, which is a low-dimensional spectrum representation. Multiple linear regression models were built for each target using the latent variables of obtained autoencoder models.

Results

R², RMSE, and RMSPE were used to show the performance of the proposed model. The created one-dimensional convolutional autoencoder model achieved a high reconstruction rate with a mean RMSPE value of 1.90% and 2.27% for calibration and prediction sets, respectively. This way, a spectrum with 700 features was converted to only 32 features. The created MLR models which use these features as input were compared to partial least squares regression and principal component regression combined with various preprocessing methods. Experimental results indicate that the proposed method has superior performance, especially in MP5 and MP6 datasets.

Unraveling propylene glycol-induced lipolysis of the biosynthesis pathway in ultra-high temperature milk using high resolution mass spectrometry untargeted lipidomics and proteomics

In July 2022, the food safety accident that excessive propylene glycol was detected in milk processing factory raised widespread concerns about quality and nutrition of milk with illegal additive. To the best of our knowledge, the influences of propylene glycol to lipids in milk had not been systematically explored. Therefore, spatiotemporal distributions of lipids related to propylene glycol reaction and changes of sensory quality were investigated by food exogenous. Briefly, 10 subclasses (Cer, DG, HexCer, LPC, LPE, PC, PE, PI, SPH and TG) included 147 lipids and 38 pivotal enzymes were annotated. Propylene glycol altered lysophospholipidase and phospholipase A₂ through altering structural order in lipids domains surrounding proteins to inhibit glycerophospholipid metabolism and initiated obvious changes in PC (10.45-27.91 mg kg^-1) and PE (12.92-49.02 mg kg^-1). This study offered insights into influences of propylene glycol doses and storage time on milk metabolism at molecular level to assess the quality of milk.

Qualitative and Quantitative Detection of Acacia Honey Adulteration with Glucose Syrup Using Near-Infrared Spectroscopy

Honey adulteration with cheap sweeteners such as corn syrup or invert syrup results in honey of lesser quality that can harm the objectives of both manufacturers and consumers. Therefore, there is a growing interest for the development of a fast and simple method for adulteration detection. In this work, near-infrared spectroscopy (NIR) was used for the detection of honey adulteration and changes in the physical and chemical properties of the prepared adulterations. Fifteen (15) acacia honey samples were adulterated with glucose syrup in a range from 10% to 90%. Raw and pre-processed NIR spectra of pure honey samples and prepared adulterations were subjected to Principal Component Analysis (PCA), Partial Least Squares (PLS) regression, and Artificial Neural Network (ANN) modeling. The results showed that PCA ensures distinct grouping of samples in pure honey samples, honey adulterations, and pure adulteration using NIR spectra after the Multiplicative Scatter Correction (MSC) method. Furthermore, PLS models developed for the prediction of the added adulterant amount, moisture content, and conductivity can be considered sufficient for screening based on RPD and RER values (1.7401 < RPD < 2.7601; 7.7128 < RER < 8.7157) (RPD of 2.7601; RER of 8.7157) and can be moderately used in practice. The R2validation of the developed ANN models was greater than 0.86 for all outputs examined. Based on the obtained results, it can be concluded that NIR coupled with ANN modeling can be considered an efficient tool for honey adulteration quantification.

Spectroscopic techniques for authentication of animal origin foods

Milk and milk products, meat, fish and poultry as well as other animal derived foods occupy a pronounced position in human nutrition. Unfortunately, fraud in the food industry is common, resulting in negative economic consequences for customers as well as significant threats to human health and the external environment. As a result, it is critical to develop analytical tools that can quickly detect fraud and validate the authenticity of such products. Authentication of a food product is the process of ensuring that the product matches the assertions on the label and complies with rules. Conventionally, various comprehensive and targeted approaches like molecular, chemical, protein based, and chromatographic techniques are being utilized for identifying the species, origin, peculiar ingredients and the kind of processing method used to produce the particular product. Despite being very accurate and unimpeachable, these techniques ruin the structure of food, are labor intensive, complicated, and can be employed on laboratory scale. Hence the need of hour is to identify alternative, modern instrumentation techniques which can help in overcoming the majority of the limitations offered by traditional methods. Spectroscopy is a quick, low cost, rapid, non-destructive, and emerging approach for verifying authenticity of animal origin foods. In this review authors will envisage the latest spectroscopic techniques being used for detection of fraud or adulteration in meat, fish, poultry, egg, and dairy products. Latest literature pertaining to emerging techniques including their advantages and limitations in comparison to different other commonly used analytical tools will be comprehensively reviewed. Challenges and future prospects of evolving advanced spectroscopic techniques will also be descanted.

Mid-Infrared (MIR) Spectroscopy for the quantitative detection of cow’s milk in buffalo milk

In Italy, buffalo mozzarella is a largely sold and consumed dairy product. The fraudulent adulteration of buffalo milk with cheaper and more available milk of other species is very frequent. In the present study, Fourier transform infrared spectroscopy (FTIR), in combination with multivariate analysis by partial least square (PLS) regression, was applied to quantitatively detect the adulteration of buffalo milk with cow milk by using a fully automatic equipment dedicated to the routine analysis of the milk composition. To enhance the heterogeneity, cow and buffalo bulk milk was collected for a period of over three years from different dairy farms. A total of 119 samples were used for the analysis to generate 17 different concentrations of buffalo-cow milk mixtures. This procedure was used to enhance variability and to properly randomize the trials. The obtained calibration model showed an R² ≥ 0.99 (R²cal. = 0.99861; root mean square error of cross-validation [RMSEC] = 2.04; R²val. = 0.99803; root mean square error of prediction [RMSEP] = 2.84; root mean square error of cross-validation [RMSECV] = 2.44) suggesting that this method could be successfully applied in the routine analysis of buffalo milk composition, providing rapid screening for possible adulteration with cow’s milk at no additional cost.

Comprehensive quantitation of multi-signature peptides originating from casein for the discrimination of milk from eight different animal species using LC-HRMS with stable isotope labeled peptides

Milk species adulteration has become an altering issue worldwide. In this study, a robust quantification method based on LC-HRMS for the simultaneous detection and differentiation of milk type from eight different animal species (namely: cow, water buffalo, wild yak, goat, sheep, donkey, horse, and camel) was established by detecting nine signature peptides originating from casein. The developed method was in-house validated in terms of sensitivity, accuracy, and precision. As a result, limits of quantification (LOQ) were ranging from 5 to 30 µg/L, recoveries ranged from 95.2% to 104.5%, and intra-day and inter-day variability were lower than 11.4% and 12.6%, respectively, for all the targeted peptides. Furthermore, this method was successfully applied to 46 commercial minor species' milk, in which 15 samples were false labeling. The obtained results indicate the necessity to monitor milk species adulteration in order to protect consumers from consuming misleading labeled minor species animal's milk.

NIR robustness model of variable selection investigation of critical quality attributes coupled with different simulate noises by prediction capability and reproducibility

variable selection is critical to select characteristic variables of critical quality attributes to improve model performance and interpret the identified variables in multivariate calibration. However, classical variable selection methods were developed and optimized by the prediction error. It is rare for the robustness evaluation of variable selection methods. In this study, the robustness of four different variable selection methods was investigated by adding different types of simulate noises to validation set and calibration and validation sets, respectively. The reproducibility as well as root mean squared error of prediction (RMSEP) were used together as common measure in assessing the robustness of different variable selection methods. The robustness of four variable selection methods method was investigated using two near infrared (NIR) datasets including open-source dataset of corn and Chinese herbal medicine (CHM) dataset. The result illustrated that variable importance in projection (VIP) was substantially more robust to additive noise, with smaller RMSEP value and high reproducibility. This provides a novel strategy for the reliability evaluation of variable selection methods in NIR model of critical quality attributes.

Milk Components by In-Line Fiber Optic Probe-Based FT-NIR: Commercial Scale Evaluation of a Potential Alternative Measurement Approach for Milk Payment

BACKGROUND

Mid-infrared (MIR) spectroscopy has traditionally been used to determine the macronutrients in bovine milk, as the basis of milk payment. Recent studies have demonstrated that NIR/FT-NIR spectroscopic systems can not only achieve MIR measurement performance, but are also generally simpler, more robust, and thus much more amenable to actual industrial process applications.

OBJECTIVE

The goal of this unique study was to investigate the feasibility of in-line FT-NIR spectroscopy for milk fat, protein, and total solids (TS) determination in a large industrial dairy processing facility, as an alternative basis for milk payment.

METHOD

Multivariant chemometric models using partial least squares (PLS) regression were built to predict the milk components. Over 1000 composite FT-NIR results gathered from the milk unloading process were compared directly to independent third-party FT-IR results.

RESULTS

Accuracy, precision, and linearity of the method were shown by Standard Error of Prediction (SEP) and Range/SEP of individual components. The SEP for fat, protein, and TS models were 0.09, 0.11, and 0.52, respectively. Range/SEP were 25.10, 12.60, and 6.40 for fat, protein, and TS, respectively. Accuracy and precision for the three components were further evaluated by the mean differences (0.01, 0.05, and 0.51) from dairy FT-IR results and the standard deviations of the mean difference (0.09, 0.09, and 0.13). Robustness was demonstrated by evaluating milk with natural variation over 6 months and using multiple instrumentation setups. The repeatability was also evaluated.

CONCLUSIONS

Overall, the in-line FT-NIR technology was found to have accurate, reliable, consistent performance similar to dairy FT-IR technology.

Detection of Adulteration in Infant Formula Based on Ensemble Convolutional Neural Network and Near-Infrared Spectroscopy

Adulteration in dairy products has received world-wide attention, and at the same time, near infrared (NIR) spectroscopy has proven to be a promising tool for adulteration detection given its advantages of real-time response and non-destructive analysis. Regardless, the accurate and robust NIR model for adulteration detection is hard to achieve in practice. Convolutional neural network (CNN), as a promising deep learning architecture, is difficult to apply to such chemometrics tasks due to the high risk of overfitting, despite the breakthroughs it has made in other fields. In this paper, the ensemble learning method based on CNN estimators was developed to address the overfitting and random initialization problems of CNN and applied to the determination of two infant formula adulterants, namely hydrolyzed leather protein (HLP) and melamine. Moreover, a probabilistic wavelength selection method based on the attention mechanism was proposed for the purpose of finding the best trade-off between the accuracy and the diversity of the sub-models in ensemble learning. The overall results demonstrate that the proposed method yielded superiority regression performance over the comparison methods for both studied data sets, and determination coefficients (R²) of 0.961 and 0.995 were obtained for the HLP and the melamine data sets, respectively.

Metataxonomic analysis of microbiota from Pakistani dromedary camelids milk and characterization of a newly isolated Lactobacillus fermentum strain with probiotic and bio-yogurt starter traits

This study was undertaken to investigate the starter and probiotic potential of lactic acid bacteria isolated from dromedarian camel's milk using both culture-dependent and -independent approaches and metataxonomic analysis. Strains of lactic acid bacteria recovered were examined in vitro for tolerance to gastric acidity, bile, and lysozyme. Bile salt hydrolysis, serum cholesterol-lowering, oxalate degradation, proteolytic activity, exopolysaccharide production, and cell surface characteristics necessary for colonizing intestinal mucosa were also evaluated. A single strain of the species, Lactobacillus fermentum named NPL280, was selected through multivariate analysis as it harbored potential probiotic advantages and fulfilled safety criteria. The strain assimilated cholesterol, degraded oxalate, produced exopolysaccharides, and proved to be a proficient alternate yogurt starter with good viability in stored bio-yogurt. A sensorial analysis of the prepared bio-yogurt was also found to be exemplary. We conclude that the indigenous L. fermentum strain NPL280 has the desired traits of a starter and adjunct probiotic culture for dairy products.

Discrimination of milk species using Raman spectroscopy coupled with partial least squares discriminant analysis in raw and pasteurized milk

BACKGROUND

Heat treatment is the most common practice for the microbiological safety of milk; hence, determination of the heat treatment of milk is essential. Also, mislabeling or adulteration of expensive milk samples, like ewe or goat milk, with cow's milk is a growing problem in the dairy market. Thus, the determination of the authenticity of milk samples has crucial importance for both producers and consumers. The aim of this study was to discriminate milk samples using Raman spectroscopy with partial least squares discriminant analysis (PLS-DA), first with regard to whether the milk was heat-treated or not, and second with regard to species (cow, goat, ewe, mixture (adulterated)) in both raw and pasteurized milk.

RESULTS

First, discrimination of milk samples as raw or pasteurized was achieved using PLS-DA. Both in calibration and prediction models, high sensitivity and specificity values were obtained for raw and pasteurized milk samples. Second, the proposed method also discriminated milk samples according to their species (cow, goat, ewe, and mixture) for both raw and pasteurized milk. In both calibration and prediction models, the sensitivity and specificity values were above 0.857 and 0.897 respectively. Also, the accuracy values were above 0.915. The results obtained denote satisfactory accurate classification of the samples.

CONCLUSION

The results suggest that Raman spectroscopy coupled with PLS-DA can be successfully used to discriminate milk samples according to heat treatment (raw/pasteurized) and their species within 20 s per sample. It was seen that Raman spectra provide valuable information to be used especially for discrimination of milk samples according to their origin. © 2020 Society of Chemical Industry.

Robust Fourier transformed infrared spectroscopy coupled with multivariate methods for detection and quantification of urea adulteration in fresh milk samples

Urea is added as an adulterant to give milk whiteness and increase its consistency for improving the solid not fat percentage, but the excessive amount of urea in milk causes overburden and kidney damages. Here, an innovative sensitive methodology based on near-infrared spectroscopy coupled with multivariate analysis has been proposed for the robust detection and quantification of urea adulteration in fresh milk samples. In this study, 162 fresh milk samples were used, those consisting 20 nonadulterated samples (without urea) and 142 with urea adulterant. Eight different percentage levels of urea adulterant, that is, 0.10%, 0.30%, 0.50%, 0.70%, 0.90%, 1.10%, 1.30%, and 1.70%, were prepared, each of them prepared in triplicates. A Frontier NIR spectrophotometer (BSEN60825-1:2007) by Perkin Elmer was used for scanning the absorption of each sample in the wavenumber range of 10,000-4,000 cm-1, using 0.2 mm path length CaF2 sealed cell at resolution of 2 cm-1. Principal components analysis (PCA), partial least-squares discriminant analysis (PLS-DA), and partial least-squares regressions (PLSR) methods were applied for the multivariate analysis of the NIR spectral data collected. PCA was used to reduce the dimensionality of the spectral data and to explore the similarities and differences among the fresh milk samples and the adulterated ones. PLS-DA also showed the discrimination between the nonadulterated and adulterated milk samples. The R-square and root mean square error (RMSE) values obtained for the PLS-DA model were 0.9680 and 0.08%, respectively. Furthermore, PLSR model was also built using the training set of NIR spectral data to make a regression model. For this PLSR model, leave-one-out cross-validation procedure was used as an internal cross-validation criteria and the R-square and the root mean square error (RMSE) values for the PLSR model were found as 0.9800 and 0.56%, respectively. The PLSR model was then externally validated using a test set. The root means square error of prediction (RMSEP) obtained was 0.48%. The present proposed study was intended to contribute toward the development of a robust, sensitive, and reproducible method to detect and determine the urea adulterant concentration in fresh milk samples.

Fraud in Animal Origin Food Products: Advances in Emerging Spectroscopic Detection Methods over the Past Five Years

Animal origin food products, including fish and seafood, meat and poultry, milk and dairy foods, and other related products play significant roles in human nutrition. However, fraud in this food sector frequently occurs, leading to negative economic impacts on consumers and potential risks to public health and the environment. Therefore, the development of analytical techniques that can rapidly detect fraud and verify the authenticity of such products is of paramount importance. Traditionally, a wide variety of targeted approaches, such as chemical, chromatographic, molecular, and protein-based techniques, among others, have been frequently used to identify animal species, production methods, provenance, and processing of food products. Although these conventional methods are accurate and reliable, they are destructive, time-consuming, and can only be employed at the laboratory scale. On the contrary, alternative methods based mainly on spectroscopy have emerged in recent years as invaluable tools to overcome most of the limitations associated with traditional measurements. The number of scientific studies reporting on various authenticity issues investigated by vibrational spectroscopy, nuclear magnetic resonance, and fluorescence spectroscopy has increased substantially over the past few years, indicating the tremendous potential of these techniques in the fight against food fraud. It is the aim of the present manuscript to review the state-of-the-art research advances since 2015 regarding the use of analytical methods applied to detect fraud in food products of animal origin, with particular attention paid to spectroscopic measurements coupled with chemometric analysis. The opportunities and challenges surrounding the use of spectroscopic techniques and possible future directions will also be discussed.

Prediction of Talc Content in Wheat Flour Based on a Near-Infrared Spectroscopy Technique

A procedure for the prediction of talc content in wheat flour based on radial basis function (RBF) neural network and near-infrared spectroscopy (NIRS) data is described. In this study, 41 wheat flour samples adulterated with different concentrations of talc were used. The diffuse reflectance spectra of all samples were collected by NIRS analyzer in the spectral range of 400 to 2,500 nm. A sample of outliers was eliminated by Mahalanobis distance based on near-infrared spectral scanning, and the remaining 40 wheat flour samples were used for spectral characteristic analysis. A calibration set of 26 samples and a prediction set of 14 samples of wheat flour were built as a result of sample set partitioning based on joint x-y distances division. A comparison of Savitzky-Golay smoothing, multiplicative scatter correction (MSC), first derivation, second derivation, and standard normal variation in the modeling showed that MSC has the best preprocessing effect. To develop a simpler, more efficient prediction model, the correlation coefficient method (CCM) was used to reduce spectral redundancy and determine the maximum correlation informative wavelength (MIW). From the full 1,050 wavelengths, 59 individual MIWs were finally selected. The optimal combined detection model was CCM-MSC-RBF based on the selected MIWs, with a determination of prediction coefficients of prediction (Rp) of 0.9999, root-mean-square error of prediction of 0.0765, and residual predictive deviation of 65.0909. The study serves as a proof of concept that NIRS technology combined with multivariate analysis has the potential to provide a fast, nondestructive and reliable assay for the prediction of talc content in wheat flour.

FTIR spectroscopy coupled with machine learning approaches as a rapid tool for identification and quantification of artificial sweeteners

Fourier transform infrared (FTIR) spectroscopy calibrations were developed to simultaneously determine the multianalytes of five artificial sweeteners, including sodium cyclamate, sucralose, sodium saccharin, acesulfame-K and aspartame. By combining the pretreatment of the spectrum and principal component analysis, 131 feature wavenumbers were extracted from the full spectral range for modelling to qualitative and quantitative analysis. Compared to random forest, k nearest neighbour and linear discriminant analysis, support vector machine model had better predictivity, indicating the most effective identification performance. Furthermore, multivariate calibration models based on partial least squares regression were constructed for quantifying any combinations of the five artificial sweeteners, and validated by prediction data sets. As shown by the good agreement between the proposed method and the reference HPLC for the determination of the sweeteners in beverage samples, a promising and rapid tool based on FTIR spectroscopy, coupled with chemometrics, has been performed to identify and objectively quantify artificial sweeteners.

Evaluation of human breastmilk adulteration by combining Fourier transform infrared spectroscopy and partial least square modeling

A two-step chemometric procedure was developed on the attenuated total reflection-Fourier transform infrared data of human breastmilk to detect adulteration by water or cow milk. The samples, collected from a Milk Bank, were analyzed before and after adulteration with whole, skimmed, semi-skimmed cow milk and water. A preliminary clustering via principal component analysis distinguished three classes: pure milk, milk adulterated with water, and milk adulterated with cow milk. A first partial least square-discriminant analysis (PLS-DA) classification model was built and then applied on new samples to identify the specific adulterants. The external validation on this model reached 100% of the correct identification of pure milk and 90% of the type of adulterants. In the following step, four PLS calibration models were built to quantify the amount of the adulterant detected in the classification analysis. The prediction performance of these models on new samples showed satisfactory parameters with root mean square error of prediction and percentage relative error lower than 1.38% and 3.31%, respectively.

NIR spectroscopic method for the detection of calcium carbide in artificial ripening of mangoes (Magnifera indica)

The present study presents a novel method employing Near Infrared Spectroscopy (NIR) for detection of the use of calcium carbide in artificial ripening of mangoes. Use of calcium carbide has been banned in artificial ripening of fruits as it contains traces of arsenic. Mango samples were ripened artificially using calcium carbide and compared with naturally ripened mangoes using NIR spectroscopic wavelength ranging from 600 to 1100 nm. The captured NIR spectra from mango samples were analysed using multivariate methods including principal component analysis, particle least square and successive projection algorithm. The obtained results showed distinguishing zones for naturally and artificially ripened mangoes. Furthermore, the arsenic content was obtained through ICP-MS analysis, and it was found that mangoes ripened artificially using calcium carbide have a higher content of arsenic. Hence, arsenic was used as a principal component in the analysis. The developed method is not unique to samples that were grown in any particular region or year as it and can be used universally as NIR will give the distinguishing comparison between naturally- and artificially ripened mangoes. This method is simple, non-invasive, non-destructive and rapid for detection of use of calcium carbide in the artificial ripening of mangoes.

Non destructive monitoring of the yoghurt fermentation phase by an image analysis of laser-diffraction patterns: Characterization of cow's, goat's and sheep's milk

Monitoring yogurt fermentation by the image analysis of diffraction patterns generated by the laser-milk interaction was explored. Cow's, goat's and sheep's milks were tested. Destructive physico-chemical analyses were done after capturing images during the processes to study the relationships between data blocks. Information from images was explored by applying a spectral phasor from which regions of interest were determined in each image channel. The histograms of frequencies from each region were extracted, which showed evolution according to textural modifications. Examining the image data by multivariate analyses allowed us to know that the captured variance from the diffraction patterns affected both milk type and texture changes. When regression studies were performed to model the physico-chemical parameters, satisfactory quantifications were obtained (from R² = 0.82 to 0.99) for each milk type and for a hybrid model that included them all. This proved that the studied patterns had a common fraction of variance during this processing, independently of milk type.

Identification of cow, buffalo, goat and ewe milk species in fermented dairy products using synchronous fluorescence spectroscopy

In the dairy industry, substitution of high priced milk species with low priced ones is a common practice, and determination of milk species is critical. In this study, synchronous fluorescence spectroscopy (SFS) method was developed for identification of milk species in fermented dairy products (yoghurt and cheese). Three partial least square-discriminant analysis models were developed in order to identify pure-mixed samples, milk species and binary mixture type, and partial least square (PLS) model was utilized to quantify the mixing ratio in binary mixtures. PLS models used for yoghurt and cheese samples showed that detection limits of adulteration were below 3.3%. Apart from the buffalo-cow yoghurt and goat-cow cheese, precision of the measurements was found to be below 6.2. It can be said that SFS technique is applicable on yoghurt and cheese samples as it's a less destructive and a less costly method compared to DNA and protein based conventional methods.

Tackling Fraudsters with Global Strategies to Expose Fraud in the Food Chain

Deliberate adulteration of food products is as old as food processing and production systems. Food adulteration is occurring increasingly often today. With globalization and complex distribution systems, adulteration may have a far-reaching impact and even adverse consequences on well-being. The means of the international community to confront and solve food fraud today are scattered and largely ineffective. A collective approach is needed to identify all stakeholders in the food supply chain, certify and qualify them, exclude those failing to meet applicable standards, and track food in a real time. This review provides some background into the drivers of fraudulent practices (economically motivated adulteration, food-industry perspectives, and consumers' perceptions of fraud) and discusses a wide range of the currently available technologies for detecting food adulteration followed by multivariate pattern recognition tools. Food chain integrity policies are discussed. Future directions in research, concerned not only with food adulterers but also with food safety and climate change, may be useful for researchers in developing interdisciplinary approaches to contemporary problems.

Evaluation of Yogurt Quality during Storage by Fluorescence Spectroscopy

The physico-chemical parameters including pH and viscosity, and the fluorescence signal induced by fluorescent compounds presenting in yogurts such as riboflavin and porphyrin were measured during one week’s storage at room temperature when five brands of yogurt samples were exposed to ambient air. The fluorescence spectra of yogurt showed four evident emission peaks, 525 nm, 633 nm, 661 nm, and 672 nm. To quantitatively investigate the quality of yogurt during deteriorating, a calculating method of the average rate of change (ARC) was proposed to study the relative change of fluorescence intensity in the spectral range of 600 to 750 nm associated with porphyrin and chlorin compounds. During the storage, the time evolution of two ARC, pH value, and viscosity were regular. Moreover, the ARC showed a good linear relationship with pH value and viscosity of yogurt. Further, multiple linear regression (MLR) models using two ARC as independent variables were developed to verify the dependence of fluorescence signal with pH value and viscosity, which showed a good linear relationship with an R-square of more than 85% for each class of yogurt. The results demonstrate that fluorescence spectra have a great potential to predict the quality of yogurt.

Endogenous phytohormones of frankincense producing Boswellia sacra tree populations

Boswellia sacra, an endemic tree to Oman, is exposed to man-made incisions for commercial level frankincense production, whereas unsustainable harvesting may lead to population decline. In this case, assessment of endogenous phytohormones (gibberellic acid (GA), indole-acetic acid (IAA), salicylic acid (SA) and kinetin) can help to understand population health and growth dynamics. Hence, it was aimed to devise a robust method using Near-Infrared spectroscopy (NIRS) coupled with multivariate methods for phytohormone analysis of thirteen different populations of B. sacra. NIRS data was recorded in absorption mode (10000-4000 cm-1) to build partial least squares regression model (calibration set 70%). Model was externally cross validated (30%) as a test set to check their prediction ability before the application to quantify the unknown amount of phytohormones in thirteen different populations of B. sacra. The results showed that phytohormonal contents varied significantly, showing a trend of SA>GA/IAA>kinetin across different populations. SA and GA contents were significantly higher in Pop13 (Hasik), followed by Pop2 (Dowkah)-an extreme end of B. sacra tree cover in Dhofar region. A similar trend in the concentration of phytohormones was found when the samples from 13 populations were subjected to advance liquid chromatography mass spectrophotometer and gas chromatograph with selected ion monitor analysis. The current analysis provides alternative tool to assess plant health, which could be important to in situ propagation of tree population as well as monitoring tree population growth dynamics.

Rapid detection of adulteration of milks from different species using Fourier Transform Infrared Spectroscopy (FTIR)

The aim of the studies reported in the Research Communication was to develop a rapid spectroscopic technique as an alternative method for the classification and discrimination of milk sources by Fourier transform infrared spectroscopy (FTIR). Cow, sheep and water buffalo milk samples were collected from various local milk producers in Istanbul, Turkey. In addition, various brands of packaged milk were purchased locally. Spectrums were obtained according to milk species origin and binary mixtures prepared in increments of 10% (10, 20, 30, 40, 50, 60, 70, 80 and 90%) for each sample analysed in FTIR spectroscopy. A successful milk species (cow, sheep, and water buffalo) discrimination and classification were achieved utilising Hierarchical cluster and principle component analyses (PCA) on the basis of Euclidean distance and Ward's algorithm. Amide-I (1700-1600/cm) and Amide-II (1565-1520/cm) spectral bands were used in the chemometric method. The results of the study indicated that adulteration of milk samples can be quantitatively detected by the FTIR technique in a short time with high accuracy. In conclusion, this method could be used as a new alternative technique for routine analysis in authenticity control of milk species origin.